---
title: Cognee
---

# CogneeRetriever

This will help you get started with the Cognee [retriever](/oss/langchain/retrieval). For detailed documentation of all CogneeRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.cognee.CogneeRetriever.html).

### Integration details

Bring-your-own data (i.e., index and search a custom corpus of documents):

| Retriever | Self-host | Cloud offering | Package |
| :--- | :--- | :---: | :---: |
[CogneeRetriever](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.cognee.CogneeRetriever.html) | ✅ | ❌ | langchain-cognee |

## Setup

For cognee default setup, only thing you need is your OpenAI API key.

If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```python
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
```

### Installation

This retriever lives in the `langchain-cognee` package:

```python
%pip install -qU langchain-cognee
```

```python
import nest_asyncio

nest_asyncio.apply()
```

## Instantiation

Now we can instantiate our retriever:

```python
from langchain_cognee import CogneeRetriever

retriever = CogneeRetriever(
    llm_api_key="sk-",  # OpenAI API Key
    dataset_name="my_dataset",
    k=3,
)
```

## Usage

Add some documents, process them, and then run queries. Cognee retrieves relevant knowledge to your queries and generates final answers.

```python
# Example of adding and processing documents
from langchain_core.documents import Document

docs = [
    Document(page_content="Elon Musk is the CEO of SpaceX."),
    Document(page_content="SpaceX focuses on rockets and space travel."),
]

retriever.add_documents(docs)
retriever.process_data()

# Now let's query the retriever
query = "Tell me about Elon Musk"
results = retriever.invoke(query)

for idx, doc in enumerate(results, start=1):
    print(f"Doc {idx}: {doc.page_content}")
```

## Use within a chain

Like other retrievers, CogneeRetriever can be incorporated into LLM applications via [chains](/oss/how-to/sequence/).

We will need a LLM or chat model:

<ChatModelTabs customVarName="llm" />

```python
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
```

```python
from langchain_cognee import CogneeRetriever
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Instantiate the retriever with your Cognee config
retriever = CogneeRetriever(llm_api_key="sk-", dataset_name="my_dataset", k=3)

# Optionally, prune/reset the dataset for a clean slate
retriever.prune()

# Add some documents
docs = [
    Document(page_content="Elon Musk is the CEO of SpaceX."),
    Document(page_content="SpaceX focuses on space travel."),
]
retriever.add_documents(docs)
retriever.process_data()


prompt = ChatPromptTemplate.from_template(
    """Answer the question based only on the context provided.

Context: {context}

Question: {question}"""
)


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)
```

```python
answer = chain.invoke("What companies do Elon Musk own?")

print("\nFinal chain answer:\n", answer)
```

## API reference

TODO: add link to API reference.
